Continuity of Care and the Physician-Patient Relationship

Methods

The data for our study came from an omnibus survey of the patient satisfaction of recipients of the Kentucky Medicaid program. Our study is a secondary analysis of that large data set. In 1997 a survey was mailed to a stratified random sample of adult participants (aged Ž18 years) in the Kentucky Medicaid fee-for-service program. The design followed the Dillman method, using an initial wave of surveys with reminder postcards and 2 additional waves of surveys to nonresponders.¹⁷ The response rate to the survey was 60%, with a total sample of 2308.

The survey items were based on the Health Employer Data and Information Set (HEDIS 3.0) customer satisfaction survey,¹⁸ the Consumer Assessments of Health Plans Study (CAHPS)¹⁹ completed by the Agency for Health Care Policy and Research (AHCPR), and satisfaction surveys used in Kentucky during previous assessments in 1987, 1989, and 1991 (KENPAC program). The internal review board of the University of Kentucky approved the survey.

Our sample was limited to individuals who visited primary care physicians often enough to assess continuity. They reported utilization on a single survey item assessing outpatient visits. A total of 1726 respondents reported making 2 or more visits to a physician’s office, clinic, or ED during the previous 12 months. In this group 404 reported having asthma. The prevalence of asthma in this population is higher than in the general population but not exceptionally higher than that found in Medicaid programs.²⁰ Continuity was measured by the question “Over the past 12 months, when you went for medical care, how often did you see the same doctor or provider?” The 4 response categories were “always,” “most of the time,” “sometimes,” and “rarely or never” (reverse scored from 1=”rarely or never” to 4=”always”). Although continuity can be measured in a variety of ways, patient self-reports have commonly been used.^1,21 In the present context, perceptions of continuity may be no less important than actual continuity in predicting patients’ evaluative ratings of physician-patient interaction.

The outcome measures were patient assessments of the health care they had received in Medicaid programs during the past 12 months. They are consistent with other self-report measures and were created for the CAHPS by the AHCPR (now the Agency for Healthcare Research and Quality). The present survey reference to 12 months differed from the original CAHPS survey reference to 6 months. The measures included an item about provider communication (“Doctor or provider listened to you and talked with you about your care”) and an item about patient influence (“Your ability to influence the treatment you received from a doctor or provider for your health problems”). These were measured on 5-point scales (reverse scored from 1=poor to 5=excellent).

Analysis

We computed bivariate analyses comparing characteristics of the groups of patients with and without asthma and assessing the relation between continuity of care and physician-patient interaction for each group (chi-square, Student t test). Then the relation between continuity of care and physician-patient interaction was evaluated in multivariate linear regression analyses in the presence of the following control variables: age, sex, education, race, number of visits, general health, health improvement, and life satisfaction. Among the variables available for analysis, these were identified as most likely to confound the relation between continuity of care and patient perceptions of the physician-patient relationship. General health (“In general, would you say your health is:”), health improvement (“Compared to one year ago, how would you rate your health in general now?”), and life satisfaction (“Overall, how satisfied or dissatisfied are you with how your life is going?”) were rated using 5-point scales coded so that higher scores mean better health, more improved health, and greater overall life satisfaction, respectively. We performed separate linear regression models for the patients with and without asthma and examined the contribution of continuity to each model. Linear regression models with all respondents combined were also performed, and interaction terms were entered for asthma status interacting with the other independent variables. Only respondents who had complete data on all items could be included in the regression analyses. We conducted all analyses with SAS statistical programming software release 6.09 (SAS Institute, Inc; Cary, NC), using complete data for each item.

Results

The characteristics of the respondents appear in Table 1. Their demographic characteristics were typical for the Medicaid population in Kentucky. Although most of the respondents were white, more of the patients with asthma were white than were those without the condition. In addition, the asthma patients reported higher numbers of health care visits and poorer health than those without asthma. Reported continuity of care and respondents’ perceptions of provider communication and patient influence are shown in Table 2. The respondents with and without asthma did not differ on these variables of interest. More than half of the respondents (58.8%) reported always seeing the same health care provider in the past 12 months (scale mean ± standard deviation=3.5 ± 0.7).